关于向量集值优化的Benson真有效性

对广义锥次数凸向量集值优化问题Benson真有效性解的标量化问题进行了研究,借助于一种新的择一性定理建立了广义锥次类凸向是集值优化问题Benson真有效解的Lagrange乘子型定理并讨论了乘子型对偶问题。     

向量优化问题的线性标量化方法和Lagrange乘子研究

向量优化是数学规划一个重要分支,其理论与方法不仅与很多学科有密切联系,而且在新兴的多学科交叉领域中有着广泛的应用.本文从向量值广义凸映射、择一定理、线性标量化方法和Lagrange乘子存在性定理等4个方面对这一领域的研究进展情况及所用方法作了较为系统的总结.首先,介绍基于像空间方法的一类广义凸向量值映射和集值映射,总结已有的广义凸映射之间的关系.其次,介绍线性系统下择一定理到非线性系统下择一定理的发展,重点总结凸性或广义凸性条件下的择一定理研究.同时,针对择一定理的应用,给出向量优化问题各种解在凸或广义凸性条件下的线性标量化方法,进而总结向量优化问题的解,特别是真有效解的Lagrange乘子存在性结果.     

广义锥-s次类凸向量优化的标量化和鞍点定理

讨论序拓扑向量空间中的约束向量优化问题.在广义锥-s次类凸假设下,得到了向量优化问题关于δ-弱有效解的标量化定理和Lagrange泛函的鞍点定理.     

Hilbert空间中带约束优化问题的广义Largrange泛函及乘子方法

Bertsekas总结了有限维空间的乘子方法。Rockafellar对线性矢量空间中带有限个不等式的凸规划问题提出了相应的乘子方法。Wierzbicki和Kurcyusz研究了Hilbert空间中带算子不等式优化问题,只给出弱逼近极小序列的结果。本文讨论Hilbert空间中带约束优化问题的广义Lagrange泛函以及乘子方法,给出了鞍点方面收敛性的结果。     

集值映射向量优化问题的ε—超有效解

本文引进了集值映射向量优化问题的ε－超有效解概念,并在集值映射为近似广义锥次似凸的假设下,建立了关于ε－超有效解的标量化定理和Lagrange乘子定理。     

非凸集值优化问题E-Benson真有效元的最优性条件

本文首先给出了集合为近似E-次类凸的等价刻画.其次,分别在锥具有紧基和弱紧基的条件下,获得了近似E-次类凸集值优化问题的E-Benson真有效元的Lagrange乘子定理.作为应用,获得了集值优化问题Benson真有效元的Lagrange乘子定理.最后,给出了集值优化问题E-鞍点的充分条件.     

线性拓扑空间中向量极值问题的广义 Kuhn-Tucker 条件

文[1]对 n 维欧氏空间 R~n,建立了在次似凸(Subconvexlike)映射下的择一定理,并以此证明具有弱凸性的极大极小定理.本文将择一定理推广到序线性拓扑空间,从而得出向量极值问题的广义 Kuhn-Tucker 条件和 Lagrange 乘子存在定理.     

集值映射向量优化问题的ε-超鞍点和ε-对偶定理

本文研究集值映射向量优化问题的ε-超鞍点和ε-对偶定理。在集值映射是近似广义锥次似凸的假设下，利用ε-超有效解的标量化和Lagrange乘子定理，建立和证明了关于ε-超有效解的鞍点和对偶定理。     

从常步长梯度方法的视角看不可微凸优化增广Lagrange方法的收敛性

增广Lagrange方法是求解非线性规划的一种有效方法.从一新的角度证明不等式约束非线性非光滑凸优化问题的增广Lagrange方法的收敛性.用常步长梯度法的收敛性定理证明基于增广Lagrange函数的对偶问题的常步长梯度方法的收敛性,由此得到增广Lagrange方法乘子迭代的全局收敛性.     

序线性空间中广义半似凸集值映射向量优化的Benson真有效性

在序线性空间中建立了广义半似凸集值映射的择一定理.利用向量闭包,引进了集值优化的Benson真有效解.在广义半似凸的假设下,获得了Benson真有效性意义下的标量化定理,Lagrangian乘子定理和鞍点定理.     

The infinite dimensional Lagrange multiplier rule for convex optimization problems

In this paper an infinite dimensional generalized Lagrange multipliers rule for convex optimization problems is presented and necessary and sufficient optimality conditions are given in order to guarantee the strong duality. Furthermore, an application is presented, in particular the existence of Lagrange multipliers associated to the bi-obstacle problem is obtained.     

On the boundedness of Lagrange multipliers of the Kuhn-Tucker theorem applied to the problem of Tikhonov function minimization

In the proof of the convergence of sequences of approximations derived by the regularized method of linearization, the Kuhn-Tucker theorem with bounded sequences of Lagrange multipliers is applied to sequences of Tikhonov functions. This paper demonstrates that in the case of three existing forms of constrains: (i) functional inequalities strict at some point, (ii) linear functional inequalities, and (iii) a linear operator equality, there exist bounded sequences of Lagrange multipliers of the Kuhn-Thucker theorem applied to the sequences of Tikhonov functions.     

高阶拉氏乘子法和弹性理论中更一般的广义变分原理

作者曾指出[1],弹性理论的最小位能原理和最小余能原理都是有约束条件限制下的变分原理采用拉格朗日乘子法,我们可以把这些约束条件乘上待定的拉氏乘子,计入有关变分原理的泛函内,从而将这些有约束条件的极值变分原理,化为无条件的驻值变分原理.如果把这些待定拉氏乘子和原来的变量都看作是独立变量而进行变分,则从有关泛函的驻值条件就可以求得这些拉氏乘子用原有物理变量表示的表达式.把这些表达式代入待定的拉氏乘子中,即可求所谓广义变分原理的驻值变分泛函.但是某些情况下,待定的拉氏乘子在变分中证明恒等于零.这是一种临界的变分状态.在这种临界状态中,我们无法用待定拉氏乘子法把变分约束条件吸收入泛函,从而解除这个约束条件.从最小余能原理出发,利用待定拉氏乘子法,企图把应力应变关系这个约束条件吸收入有关泛函时,就发生这种临界状态,用拉氏乘子法,从余能原理只能导出Hellinger-Reissner变分原理[2],[3],这个原理中只有应力和位移两类独立变量,而应力应变关系则仍是变分约束条件,人们利用这个条件,从变分求得的应力中求应变.所以Hellinger-Reissner变分原理仍是一种有条件的变分原理.     

A complete characterization of strong duality in nonconvex optimization with a single constraint

We first establish sufficient conditions ensuring strong duality for cone constrained nonconvex optimization problems under a generalized Slater-type condition. Such conditions allow us to cover situations where recent results cannot be applied. Afterwards, we provide a new complete characterization of strong duality for a problem with a single constraint: showing, in particular, that strong duality still holds without the standard Slater condition. This yields Lagrange multipliers characterizations of global optimality in case of (not necessarily convex) quadratic homogeneous functions after applying a generalized joint-range convexity result. Furthermore, a result which reduces a constrained minimization problem into one with a single constraint under generalized convexity assumptions, is also presented.     

多目标规划的 Lagrange 对偶与标量化定理

定义与问题设 K(?)R~p 为内部非空的点锥,则 K 在 R~p 上确定了如下偏序:x≦K.y(?)y-x∈K,xk(?)}.     

Characterizations of the Benson Proper Efficiency for Nonconvex Vector Optimization

Under generalized cone-subconvexlikeness for vector-valued mappings in locally-convex Hausdorff topological vector spaces, a Gordan-form alternative theorem is derived. Some characterizations of the Benson proper efficiency under this generalized convexity are established in terms of scalarization, Lagrangian multipliers, saddle-point criterion, and duality.     

带摩擦的弹性接触问题广义变分不等原理的简化证明

在弹性摩擦接触问题中 ,从变分原理出发来研究接触问题 ,可以将摩擦力纳入问题的能量泛函 .为了得到摩擦约束弹性接触问题的能量泛函 ,日前大多是用拉格朗日乘子法 ,但拉格朗日方法用在变分不等问题中 ,要利用非线性泛函分析和凸分析来证明 ,证明复杂 .本文利用向量分析的工具及巧妙的变换 ,对带摩擦约束的弹性接触问题的广义变分不等原理进行了严格的证明 ,由于只用到向量分析 ,简化了证明 .     

Generalized (<Emphasis Type="Italic">F</Emphasis>,<Emphasis Type="Italic">ρ</Emphasis>)-Convexity and Duality in Nonsmooth Problems of Multiobjective Optimization

A mixed-type dual for a nonsmooth multiobjective optimization problem with inequality and equality constraints is formulated. We obtain weak and strong duality theorems for a mixed-type dual without requiring the regularity assumptions and the nonnegativeness of the Lagrange multipliers associated to the equality constraints. We apply also a nonsmooth constraint qualification for multiobjective programming to establish strong duality results. In this case, our constraint qualification assures the existence of positive Lagrange multipliers associated with the vector-valued objective function. This work was supported by Center of Excellence for Mathematics, University of Isfahan, Isfahan, Iran.     

Duality Theorems for Separable Convex Programming Without Qualifications

In the research of mathematical programming, duality theorems are essential and important elements. Recently, Lagrange duality theorems for separable convex programming have been studied. Tseng proves that there is no duality gap in Lagrange duality for separable convex programming without any qualifications. In other words, although the infimum value of the primal problem equals to the supremum value of the Lagrange dual problem, Lagrange multiplier does not always exist. Jeyakumar and Li prove that Lagrange multiplier always exists without any qualifications for separable sublinear programming. Furthermore, Jeyakumar and Li introduce a necessary and sufficient constraint qualification for Lagrange duality theorem for separable convex programming. However, separable convex constraints do not always satisfy the constraint qualification, that is, Lagrange duality does not always hold for separable convex programming. In this paper, we study duality theorems for separable convex programming without any qualifications. We show that a separable convex inequality system always satisfies the closed cone constraint qualification for quasiconvex programming and investigate a Lagrange-type duality theorem for separable convex programming. In addition, we introduce a duality theorem and a necessary and sufficient optimality condition for a separable convex programming problem, whose constraints do not satisfy the Slater condition.